Estimating/specifying one's own position (place) in an environment is an ability indispensable for a person or a machine. It is always important for a robot or a computer vision to recognise where it is located. In particular, in the case of a movable robot, recognizing where the robot itself is located is a fundamental requirement of its navigation system.
For such place identification, how accurately feature values of a surrounding environment are extracted is a key point. As conventional techniques for extracting local feature values from environments, there are affine-invariant feature values (MSER, Harris-Affine, Hessian-Affine, Salient Region, and so on), and feature values that are invariant to size changes (SIFT: Scale Invariant Feature Transformation, SURF: Speed Up Robustness Features, and so on). Further, as feature values that are obtained by extracting only feature values robust against changes in shooting positions from local feature values extracted from each of successive images by the above-described techniques, there are PIRF (Position-invariant Robust Features) (Patent Literatures 1 to 3, and Non-patent Literature 1).